Research And Application Of Intelligent Detection Algorithm Of Track Damage Based On Convolutional Neural Network

Railway has always been the main mode of transportation in China.Especially in recent years,with the significant improvement of national industrial production capacity and the strong support of relevant policies,the overall scale of railway line network has become larger and larger,and the rail transit industry has gradually entered the stage of paying equal attention to development and construction and operation and maintenance,Thus,there is a contradiction between the traditional manual inspection and detection method with low efficiency and high cost and the actual demand of the railway operation line network with such a large scale of maintenance and repair.How to scientifically and efficiently ensure the stable and safe operation of railway rail transit is a problem that must be solved at this stage.This dissertation mainly focuses on the surface images of the actual railway track,Through the research and development of artificial intelligence detection methods such as deep learning、machine vision and convolution neural network,the intelligent analysis of the railway track images is realized,and the evaluation report of the integrity of the railway track structure is obtained timely and accurately.It plays a positive role in ensuring the safety and stability of the railway system and improves the economic and social benefits of rail transit operation.This dissertation mainly studies the following aspects:1.This dissertation expounds the necessity and research significance of intelligent analysis of railway track images,puts forward three main research directions based on convolution neural network model: image automatic classification,fastener target location and recognition and track slab crack semantic segmentation.Meanwhile,determine the selection of railway image acquisition machine to provide support for the construction of network model training images database.2.Aiming at the influence of the complex railway field environment and the change of railway track type on the training of convolution neural network model,proposed an automatic classification method of railway track images based on Efficient Net.The activation function of the original model is improved and the post-processing optimization algorithm is added to improve the accuracy and efficiency of classification.The track images are divided into four types to provide data support for the subsequent intelligent detection in this dissertation.3.Aiming at the poor detection effect caused by the small proportion of pixels of railway fasteners target compared with the overall track image,proposed an automatic positioning and detection method of track fasteners based on YOLOv3 algorithm.Firstly,the K-means clustering algorithm is used to determine the size of the preset boundary box,and then the backbone network Dark Net-53 of the detection model is expanded to increase its output scale,so as to expand the receptive field of the fastener target and improve the detection accuracy of the railway fasteners target.4.Aiming at the problem that the pixel characteristics of damage cracks are not obvious due to the complex background environment of track plate,proposed a track plate cracks segmentation and detection method based on Dense Net.After using Labelme software to mark the cracks area of track plate and establish the data set,the feature extraction structure of Dense Net is improved by using hole convolution and spatial pyramid pooling module to increase the feature retention of cracks area,so as to realize the accurate segmentation of small cracks.5.Combined with the actual field application requirements,the track damage intelligent identification system is developed with Python programming language.The intelligent detection functions involved in this dissertation,such as railway track image classification,fastener target positioning and recognition and track slab crack semantic segmentation,are integrated and encapsulated.The collected image data can be online detected by software,and finally the detection report is generated to realize the intelligent detection and analysis of railway track.This dissertation provides three aspects of railway object segmentation theory,image classification and maintenance.At the same time,the intelligent identification system is developed by integrating three functions,After the actual field application test,the system runs stably,the detection effect is similar to that in the laboratory,and has practical engineering application value.